If you think it is, that's fine. We might as well just taking the same approach to crime, and start locking individuals up or not extending job offers, NOT because they've done a single thing wrong, but simply because they're statistically more likely to.
The main difference is that people have right to trial and to be considered innocent until proven guilty. But there is no 'right to credit'. Credit is fundamentally two-party contract.
Also there is shared limit to risk, forcing creditors to take more risk with some people means they may not take that risk in other cases (not giving credit to someone who would be marked lower risk with more informed decision) or forcing them to raise credit cost to everybody.
The crime analogy is inappropriate. Insurance is a private voluntary arrangement between two consenting entities. Convictions on the other hand are an involuntary imposition on an unwilling party.
One could make the argument that allowing inherent traits in the pricing of insurance is the less authoritarian and more utilitarian option, since it is less forceful state interference in private business and leads to more accurate claims pricing and less subsidisation of insurance for person A by person B. Your prison sentencing analogy on the other hand implies more force, which is why I don't view it as a valid analogy.
The ethical argument can go either way depending on the axioms we pick a priori. If we pick Libertarian deontological axioms, then the ethical choice is to allow inherent traits into the model. If we pick racial equity deontological axioms, then we get another conclusion.
Which is an important thing that isn’t math.
Consider a sample size of 10/1million with height value of 7m. And somehow 7/10 had poor ability to repay loans. With such a small sample size of this relevant factor be a good thing to rely on?
Credit risk, f(X), is an unknown population function that needs to be estimated using observed data X.
If including tallness into X improves our estimate of f(X), then we've gotten a better model.
You've asserted that X should only contain an individual's past actions instead of their inherent traits such as tallness. This may satisfy certain political objectives, and that's fine if we're being upfront about the underlying motivation, but from an ML perspective your prescription doesn't make much sense unless you have some prior knowledge about the function f(X) that tells you that tallness both isn't relevant and isn't acting as an instrumental variable for some other missing feature.
Unless you have such domain knowledge, you've little business asserting what are appropriate features to use in order to improve model quality.